In the quest for reliable and sustainable energy, wind power has emerged as a cornerstone of modern electrical systems. However, the intermittent nature of wind poses significant challenges for grid stability and market operations. Accurate wind power forecasting is crucial for integrating this renewable resource effectively into the energy mix. A recent comprehensive review published in the journal *Discover Applied Sciences* sheds light on the latest advancements in machine learning (ML) techniques for wind power prediction, offering insights that could revolutionize the energy sector.
Led by Inam Ul Haq from the Department of Computer Science and Engineering at Chandigarh University, the research delves into the evolution of ML approaches for wind power forecasting from 2006 to 2025. The study categorizes current forecasting methods into physical, statistical, traditional machine learning, deep learning, ensemble, and hybrid models. Among these, deep learning models, particularly Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and hybrid CNN–LSTM frameworks, have shown promising capabilities.
“Advanced deep learning models excel in capturing temporal dependencies and nonlinear patterns in wind data, which are critical for accurate forecasting,” Haq explains. “However, each model has its constraints and specific application domains, making it essential to understand their strengths and limitations.”
The review also highlights the importance of lightweight solutions that can be deployed on low-cost edge devices, as well as optimization algorithms like the Fruit Fly Algorithm and Particle Swarm Optimization (PSO). These innovations are pivotal for enhancing the scalability and interpretability of wind power forecasting models.
Comparative analyses reveal that different models perform variably across predicting horizons, temporal dependencies, and nonlinearity. The study identifies key challenges, including data quality issues, computational limitations, and performance under extreme weather conditions. Despite these hurdles, the findings suggest that future research should focus on IoT-enabled sensor networks, multi-model fusion, physics-informed learning, and advanced structures like Transformers to improve prediction accuracy and robustness.
The implications for the energy sector are profound. Accurate wind power forecasting can optimize grid management, reduce energy costs, and enhance the reliability of renewable energy integration. As Haq notes, “The development of intelligent, efficient, and sustainable renewable energy systems hinges on our ability to leverage advanced machine learning techniques effectively.”
This comprehensive review not only charts the evolving landscape of ML-driven wind power forecasting but also provides practical guidance for energy professionals and researchers. By addressing the current challenges and highlighting future directions, the study paves the way for more reliable and efficient wind energy integration, ultimately contributing to a more sustainable energy future.
Published in *Discover Applied Sciences*, this research offers a roadmap for the energy sector to harness the full potential of wind power, ensuring a stable and sustainable energy supply for years to come.